The invention relates to a coronary artery sequence blood vessel segmentation method based on space-time discriminative feature learning, which is used for carrying out blood vessel segmentation processing on a cardiac coronary artery angiography sequence image, and includes processing a current frame of image and several adjacent frames of images based on a pre-trained improved Unet network model, and obtaining blood vessel segmentation result of current frame image, wherein the improved Unet network model comprises a coding part, a jump connection layer and a decoding part, the coding part adopts a 3D convolution layer to perform time-space feature extraction, the decoding part is provided with a channel attention module, and the jump connection layer aggregates features extracted by thecoding part, thus obtaining an aggregation feature map and transmitting the aggregation feature map to the decoding part. Compared with the prior art, the cardiac coronary artery blood vessel segmentation method introduces the spatial-temporal features to perform cardiac coronary artery blood vessel segmentation, reduces the interference of time domain noise, emphasizes the blood vessel features,alleviates the problem of class imbalance in blood vessel segmentation, and has higher blood vessel segmentation accuracy.